
#IMPORTING LIBRARIES
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import scipy as sp
import warnings
import datetime
warnings.filterwarnings("ignore")
%matplotlib inline


import pandas as pd
data = pd.read_csv("D:\Sem 4\ml&ai\ml project\supermarket_sales - Sheet1.csv")
data


data.head()


data.describe()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 17 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   Invoice ID               1000 non-null   object 
 1   Branch                   1000 non-null   object 
 2   City                     1000 non-null   object 
 3   Customer type            1000 non-null   object 
 4   Gender                   1000 non-null   object 
 5   Product line             1000 non-null   object 
 6   Unit price               1000 non-null   float64
 7   Quantity                 1000 non-null   int64  
 8   Tax 5%                   1000 non-null   float64
 9   Total                    1000 non-null   float64
 10  Date                     1000 non-null   object 
 11  Time                     1000 non-null   object 
 12  Payment                  1000 non-null   object 
 13  cogs                     1000 non-null   float64
 14  gross margin percentage  1000 non-null   float64
 15  gross income             1000 non-null   float64
 16  Rating                   1000 non-null   float64
dtypes: float64(7), int64(1), object(9)
memory usage: 132.9+ KB


data.shape

(1000, 17)


data.dtypes

Invoice ID                  object
Branch                      object
City                        object
Customer type               object
Gender                      object
Product line                object
Unit price                 float64
Quantity                     int64
Tax 5%                     float64
Total                      float64
Date                        object
Time                        object
Payment                     object
cogs                       float64
gross margin percentage    float64
gross income               float64
Rating                     float64
dtype: object


data.columns

Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',
       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',
       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',
       'Rating'],
      dtype='object')


data.isnull().sum()

Invoice ID                 0
Branch                     0
City                       0
Customer type              0
Gender                     0
Product line               0
Unit price                 0
Quantity                   0
Tax 5%                     0
Total                      0
Date                       0
Time                       0
Payment                    0
cogs                       0
gross margin percentage    0
gross income               0
Rating                     0
dtype: int64


data.isnull().any()

Invoice ID                 False
Branch                     False
City                       False
Customer type              False
Gender                     False
Product line               False
Unit price                 False
Quantity                   False
Tax 5%                     False
Total                      False
Date                       False
Time                       False
Payment                    False
cogs                       False
gross margin percentage    False
gross income               False
Rating                     False
dtype: bool


import matplotlib.pyplot as plt

data.hist(figsize=(20,14))
plt.show()


data.corr()


import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize = (12,10))
sns.heatmap(data.corr(), annot =True)

<AxesSubplot:>


plt.figure(figsize=(14,10))
sns.set_style(style='whitegrid')
plt.subplot(2,3,1)
sns.boxplot(x='Unit price',data=data)
plt.subplot(2,3,2)
sns.boxplot(x='Quantity',data=data)
plt.subplot(2,3,3)
sns.boxplot(x='Total',data=data)
plt.subplot(2,3,4)
sns.boxplot(x='cogs',data=data)
plt.subplot(2,3,5)
sns.boxplot(x='Rating',data=data)
plt.subplot(2,3,6)
sns.boxplot(x='gross income',data=data)

<AxesSubplot:xlabel='gross income'>


# Importing
import sklearn

import pandas as pd
 
# Load the dataset
data = pd.read_csv("D:\Sem 4\ml&ai\ml project\supermarket_sales - Sheet1.csv")
 
def outliers(data, feature):
    Q1= data[feature].quantile(0.25)
    Q3 = data[feature].quantile(0.75)
    IQR = Q3 - Q1
    upper_limit = Q3 + 1.5 * IQR
    lower_limit = Q1 - 1.5 * IQR
    return upper_limit, lower_limit
upper, lower = outliers(data, "Total")
print("Upper limit: ", upper)
print("Lower limit: ", lower)

Upper limit:  991.7420625
Lower limit:  -395.9694375


data[(data['Total'] < lower) | (data['Total'] > upper)]


new_data = data[(data['Total'] > lower) & (data['Total'] < upper)]
new_data


import sklearn

import pandas as pd
 

def outliers(new_data, feature):
    Q1= new_data[feature].quantile(0.25)
    Q3 = new_data[feature].quantile(0.75)
    IQR = Q3 - Q1
    upper_limit = Q3 + 1.5 * IQR
    lower_limit = Q1 - 1.5 * IQR
    return upper_limit, lower_limit
upper, lower = outliers(new_data, "cogs")
print("Upper limit: ", upper)
print("Lower limit: ", lower)

Upper limit:  931.8249999999998
Lower limit:  -372.93499999999995


new_data[(new_data['cogs'] < lower) | (new_data['cogs'] > upper)]


new1_data = new_data[(new_data['Total'] > lower) & (new_data['Total'] < upper)]
new1_data


import sklearn

import pandas as pd
 

def outliers(new1_data, feature):
    Q1= new1_data[feature].quantile(0.25)
    Q3 = new1_data[feature].quantile(0.75)
    IQR = Q3 - Q1
    upper_limit = Q3 + 1.5 * IQR
    lower_limit = Q1 - 1.5 * IQR
    return upper_limit, lower_limit
upper, lower = outliers(new1_data, "gross income")
print("Upper limit: ", upper)
print("Lower limit: ", lower)

Upper limit:  45.8190625
Lower limit:  -18.2374375


new1_data[(new1_data['gross income'] < lower) | (new1_data['gross income'] > upper)]


new2_data = new1_data[(new1_data['gross income'] > lower) & (new1_data['gross income'] < upper)]
new2_data


sns.boxplot(x='Total',data=new2_data)
plt.title('After removing outliers in Total')

Text(0.5, 1.0, 'After removing outliers in Total')


import seaborn as sns


sns.boxplot(x='cogs',data=new2_data)
plt.title('After removing outliers in cogs')





plt.show()


import seaborn as sns
plt.figure(figsize=(7,5))


sns.boxplot(x='gross income',data=new2_data)
plt.title('After removing outliers in gross income')

Text(0.5, 1.0, 'After removing outliers in gross income')


import seaborn as sns

sns.pairplot(data=new2_data)

<seaborn.axisgrid.PairGrid at 0x24517788e50>


sns.regplot(x='Rating', y= 'gross income', data=new2_data)

<AxesSubplot:xlabel='Rating', ylabel='gross income'>


sns.regplot(x='Rating', y= 'cogs', data=new2_data)

<AxesSubplot:xlabel='Rating', ylabel='cogs'>


sns.regplot(x='Rating', y= 'Total', data=new2_data)

<AxesSubplot:xlabel='Rating', ylabel='Total'>


new2_data.columns

Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',
       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',
       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',
       'Rating'],
      dtype='object')


sns.lineplot(x='Rating', y= 'Unit price', data=new2_data)

<AxesSubplot:xlabel='Rating', ylabel='Unit price'>


plt.style.use("default")
plt.figure(figsize=(5,5))
sns.barplot(x="Rating", y="Unit price", data=new2_data[170:180])
plt.title("Rating vs Unit Price",fontsize=15)
plt.xlabel("Rating")
plt.ylabel("Unit Price")
plt.show()


new2_data.columns

Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',
       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',
       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',
       'Rating'],
      dtype='object')


plt.style.use("default")
plt.figure(figsize=(5,5))
sns.barplot(x="Rating", y="Gender", data=new2_data[170:180])
plt.title("Rating vs Gender",fontsize=15)
plt.xlabel("Rating")
plt.ylabel("Gender")
plt.show()


plt.style.use("default")
plt.figure(figsize=(5,5))
sns.barplot(x="Rating", y="Quantity", data=new2_data[170:180])
plt.title("Rating vs Quantity",fontsize=15)
plt.xlabel("Rating")
plt.ylabel("Quantity")
plt.show()


list_1=list(new2_data.columns)


list_cate=[]
for i in list_1:
    if new2_data[i].dtype=='object':
        list_cate.append(i)


from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()


for i in list_cate:
    new2_data[i]=le.fit_transform(new2_data[i])


y=new2_data['Gender']
x=new2_data.drop('Gender',axis=1)


new2_data


from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=0,test_size=0.2)


print(len(x_train))
print(len(x_test))
print(len(y_train))
print(len(y_test))

784
196
784
196


from sklearn.metrics import classification_report, confusion_matrix
from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
knn=KNeighborsClassifier(n_neighbors=7)

knn.fit(x_train,y_train)

KNeighborsClassifier(n_neighbors=7)


y1_pred=knn.predict(x_test)
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
print("Classification Report is:\n",classification_report(y_test,y1_pred))
print("Confusion Matrix:\n",confusion_matrix(y_test,y1_pred))
print("Training Score:\n",knn.score(x_train,y_train)*100)
print("Test Score:\n",knn.score(x_test,y_test)*100)
auc = metrics.roc_auc_score(y_test, y1_pred)

false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y1_pred)
# plt.subplot(2,3,1)
plt.figure(figsize=(10, 8), dpi=100)
plt.axis('scaled')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title("AUC & ROC Curve for KNN - Training")
plt.plot(false_positive_rate, true_positive_rate, 'g')
plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")

# auc = metrics.roc_auc_score(y_test, y1_pred)

# false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y1_pred)
# plt.subplot(2,3,1)
# plt.figure(figsize=(10, 8), dpi=100)
# plt.axis('scaled')
# plt.xlim([0, 1])
# plt.ylim([0, 1])
# plt.title("AUC & ROC Curve for KNN - Testing")
# plt.plot(false_positive_rate, true_positive_rate, 'g')
# plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
# plt.xlabel("False Positive Rate")
# plt.ylabel("True Positive Rate"



plt.figure(figsize=(22,6), dpi=100)

x_ax = range(len(y_test))
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y1_pred, label="predicted")
plt.title("Actual Sales and predicted data")
plt.legend()

plt.show()

Classification Report is:
               precision    recall  f1-score   support

           0       0.52      0.46      0.49       109
           1       0.41      0.47      0.44        87

    accuracy                           0.46       196
   macro avg       0.47      0.46      0.46       196
weighted avg       0.47      0.46      0.47       196

Confusion Matrix:
 [[50 59]
 [46 41]]
Training Score:
 66.45408163265306
Test Score:
 46.42857142857143


from sklearn.svm import SVC

svc = SVC()
svc.fit(x_train, y_train)

SVC()


y2_pred=svc.predict(x_test)
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
print("Classification Report is:\n",classification_report(y_test,y2_pred))
print("Confusion Matrix:\n",confusion_matrix(y_test,y2_pred))
print("Training Score:\n",svc.score(x_train,y_train)*100)
print("Test Score:\n",svc.score(x_test,y_test)*100)
auc = metrics.roc_auc_score(y_test, y2_pred)

false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y2_pred)

plt.figure(figsize=(10, 8), dpi=100)
plt.axis('scaled')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title("AUC & ROC Curve for SVM")
plt.plot(false_positive_rate, true_positive_rate, 'g')
plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")



plt.figure(figsize=(22,6))

x_ax = range(len(y_test))
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y2_pred, label="predicted")
plt.title("Actual Sales and predicted data")
plt.legend()
plt.show()

Classification Report is:
               precision    recall  f1-score   support

           0       0.52      0.39      0.45       109
           1       0.42      0.54      0.47        87

    accuracy                           0.46       196
   macro avg       0.47      0.47      0.46       196
weighted avg       0.47      0.46      0.46       196

Confusion Matrix:
 [[43 66]
 [40 47]]
Training Score:
 57.14285714285714
Test Score:
 45.91836734693878


from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(x_train,y_train)

GaussianNB()


y3_pred=gnb.predict(x_test)
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
print("Classification Report is:\n",classification_report(y_test,y3_pred))
print("Confusion Matrix:\n",confusion_matrix(y_test,y3_pred))
print("Training Score:\n",gnb.score(x_train,y_train)*100)
print("Test Score:\n",gnb.score(x_test,y_test)*100)
auc = metrics.roc_auc_score(y_test, y3_pred)

false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y3_pred)

plt.figure(figsize=(10, 8), dpi=100)
plt.axis('scaled')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title("AUC & ROC Curve for NAIVE BAYES")
plt.plot(false_positive_rate, true_positive_rate, 'g')
plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")

plt.figure(figsize=(22,6))

x_ax = range(len(y_test))
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y3_pred, label="predicted")
plt.title("Actual Sales and predicted data")
plt.legend()

Classification Report is:
               precision    recall  f1-score   support

           0       0.52      0.36      0.42       109
           1       0.42      0.59      0.49        87

    accuracy                           0.46       196
   macro avg       0.47      0.47      0.46       196
weighted avg       0.48      0.46      0.45       196

Confusion Matrix:
 [[39 70]
 [36 51]]
Training Score:
 56.76020408163265
Test Score:
 45.91836734693878

<matplotlib.legend.Legend at 0x2451e5d6e20>


from sklearn.tree import DecisionTreeClassifier
dtree = DecisionTreeClassifier(max_depth=6, random_state=123,criterion='entropy')

dtree.fit(x_train,y_train)

DecisionTreeClassifier(criterion='entropy', max_depth=6, random_state=123)


y4_pred=dtree.predict(x_test)
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
print("Classification Report is:\n",classification_report(y_test,y4_pred))
print("Confusion Matrix:\n",confusion_matrix(y_test,y4_pred))
print("Training Score:\n",dtree.score(x_train,y_train)*100)
print("Test Score:\n",dtree.score(x_test,y_test)*100)
auc = metrics.roc_auc_score(y_test, y4_pred)

false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y4_pred)

plt.figure(figsize=(10, 8), dpi=100)
plt.axis('scaled')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title("AUC & ROC Curve for DECISION TREE CLASSIFIER")
plt.plot(false_positive_rate, true_positive_rate, 'g')
plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")



plt.figure(figsize=(22,6))

x_ax = range(len(y_test))

plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y4_pred, label="predicted")
plt.title("Actual Sales and predicted data")
plt.legend()

Classification Report is:
               precision    recall  f1-score   support

           0       0.49      0.32      0.39       109
           1       0.41      0.59      0.48        87

    accuracy                           0.44       196
   macro avg       0.45      0.45      0.44       196
weighted avg       0.46      0.44      0.43       196

Confusion Matrix:
 [[35 74]
 [36 51]]
Training Score:
 66.19897959183673
Test Score:
 43.87755102040816

<matplotlib.legend.Legend at 0x2451eb13a00>


from sklearn.ensemble import RandomForestClassifier
rfc=RandomForestClassifier()
rfc.fit(x_train,y_train)

RandomForestClassifier()


y5_pred=rfc.predict(x_test)
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
print("Classification Report is:\n",classification_report(y_test,y5_pred))
print("Confusion Matrix:\n",confusion_matrix(y_test,y5_pred))
print("Training Score:\n",rfc.score(x_train,y_train)*100)
print("Test Score:\n",knn.score(x_test,y_test)*100)
auc = metrics.roc_auc_score(y_test, y5_pred)

false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y5_pred)

plt.figure(figsize=(10, 8), dpi=100)
plt.axis('scaled')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title("AUC & ROC Curve for RANDOM FOREST CLASSIFIER")
plt.plot(false_positive_rate, true_positive_rate, 'g')
plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")


plt.figure(figsize=(22,8))

x_ax = range(len(y_test))

plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y5_pred, label="predicted")
plt.title("Actual Sales and predicted data")
plt.legend()
plt.show()

Classification Report is:
               precision    recall  f1-score   support

           0       0.57      0.48      0.52       109
           1       0.46      0.55      0.50        87

    accuracy                           0.51       196
   macro avg       0.51      0.51      0.51       196
weighted avg       0.52      0.51      0.51       196

Confusion Matrix:
 [[52 57]
 [39 48]]
Training Score:
 100.0
Test Score:
 46.42857142857143


from sklearn.ensemble import ExtraTreesClassifier
etc = ExtraTreesClassifier(n_estimators=100, random_state=0)
etc.fit(x_train,y_train)

ExtraTreesClassifier(random_state=0)


y6_pred=etc.predict(x_test)
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
print("Classification Report is:\n",classification_report(y_test,y6_pred))
print("Confusion Matrix:\n",confusion_matrix(y_test,y6_pred))
print("Training Score:\n",etc.score(x_train,y_train)*100)
print("Test Score:\n",etc.score(x_test,y_test)*100)

auc = metrics.roc_auc_score(y_test, y6_pred)

false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y6_pred)

plt.figure(figsize=(10, 8), dpi=100)
plt.axis('scaled')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title("AUC & ROC Curve for RANDOM FOREST CLASSIFIER")
plt.plot(false_positive_rate, true_positive_rate, 'g')
plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")


plt.figure(figsize=(22,8))

x_ax = range(len(y_test))

plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y6_pred, label="predicted")
plt.title("Actual Sales and predicted data")
plt.legend()
plt.show()

new2_data = pd.DataFrame({'Actual': y_test, 'Predicted': y6_pred})
new2_data

Classification Report is:
               precision    recall  f1-score   support

           0       0.59      0.50      0.54       109
           1       0.47      0.55      0.51        87

    accuracy                           0.53       196
   macro avg       0.53      0.53      0.52       196
weighted avg       0.53      0.53      0.53       196

Confusion Matrix:
 [[55 54]
 [39 48]]
Training Score:
 100.0
Test Score:
 52.55102040816326


from sklearn.metrics import plot_roc_curve

disp = plot_roc_curve(knn,x_train,y_train)
plot_roc_curve(svc,x_train,y_train,ax=disp.ax_)
plot_roc_curve(gnb,x_train,y_train,ax=disp.ax_)
plot_roc_curve(dtree,x_train,y_train,ax=disp.ax_)
plot_roc_curve(rfc,x_train,y_train,ax=disp.ax_)
plot_roc_curve(etc,x_train,y_train,ax=disp.ax_)
plt.title('AUC-ROC curve for Training')
plt.show()


from sklearn.metrics import plot_roc_curve

disp = plot_roc_curve(knn,x_test,y_test)
plot_roc_curve(svc,x_test,y_test,ax=disp.ax_)
plot_roc_curve(gnb,x_test,y_test,ax=disp.ax_)
plot_roc_curve(dtree,x_test,y_test,ax=disp.ax_)
plot_roc_curve(rfc,x_test,y_test,ax=disp.ax_)
plot_roc_curve(etc,x_test,y_test,ax=disp.ax_)
plt.title('AUC-ROC curve for Testing')
plt.show()


import matplotlib.pyplot as plt
plt.figure(figsize=(22,20))
plt.ylim(-2,2)
x_ax = range(len(y_test))

plt.subplot(6,1,1)
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y1_pred, label="predicted")
plt.title("Actual Sales and predicted data - KNN")
plt.legend()

plt.subplot(6,1,2)
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y2_pred, label="predicted")
plt.title("Actual Sales and predicted data - SVM")
plt.legend()

plt.subplot(6,1,3)
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y3_pred, label="predicted")
plt.title("Actual Sales and predicted data - NAIVE BAYES")
plt.legend()

plt.subplot(6,1,4)
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y4_pred, label="predicted")
plt.title("Actual Sales and predicted data - DECISION TREE")
plt.legend()

plt.subplot(6,1,5)
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y5_pred, label="predicted")
plt.title("Actual Sales and predicted data - RANDOM FOREST CLASSIFIER")
plt.legend()

plt.subplot(6,1,6)
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax,y5_pred, label="predicted")
plt.title("Actual Sales and predicted data - EXTRA TREES CLASSIFIER")
plt.legend()

plt.show()

	Invoice ID	Branch	City	Customer type	Gender	Product line	Unit price	Quantity	Tax 5%	Total	Date	Time	Payment	cogs	gross margin percentage	gross income	Rating
0	750-67-8428	A	Yangon	Member	Female	Health and beauty	74.69	7	26.1415	548.9715	1/5/2019	13:08	Ewallet	522.83	4.761905	26.1415	9.1
1	226-31-3081	C	Naypyitaw	Normal	Female	Electronic accessories	15.28	5	3.8200	80.2200	3/8/2019	10:29	Cash	76.40	4.761905	3.8200	9.6
2	631-41-3108	A	Yangon	Normal	Male	Home and lifestyle	46.33	7	16.2155	340.5255	3/3/2019	13:23	Credit card	324.31	4.761905	16.2155	7.4
3	123-19-1176	A	Yangon	Member	Male	Health and beauty	58.22	8	23.2880	489.0480	1/27/2019	20:33	Ewallet	465.76	4.761905	23.2880	8.4
4	373-73-7910	A	Yangon	Normal	Male	Sports and travel	86.31	7	30.2085	634.3785	2/8/2019	10:37	Ewallet	604.17	4.761905	30.2085	5.3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
995	233-67-5758	C	Naypyitaw	Normal	Male	Health and beauty	40.35	1	2.0175	42.3675	1/29/2019	13:46	Ewallet	40.35	4.761905	2.0175	6.2
996	303-96-2227	B	Mandalay	Normal	Female	Home and lifestyle	97.38	10	48.6900	1022.4900	3/2/2019	17:16	Ewallet	973.80	4.761905	48.6900	4.4
997	727-02-1313	A	Yangon	Member	Male	Food and beverages	31.84	1	1.5920	33.4320	2/9/2019	13:22	Cash	31.84	4.761905	1.5920	7.7
998	347-56-2442	A	Yangon	Normal	Male	Home and lifestyle	65.82	1	3.2910	69.1110	2/22/2019	15:33	Cash	65.82	4.761905	3.2910	4.1
999	849-09-3807	A	Yangon	Member	Female	Fashion accessories	88.34	7	30.9190	649.2990	2/18/2019	13:28	Cash	618.38	4.761905	30.9190	6.6

	Invoice ID	Branch	City	Customer type	Gender	Product line	Unit price	Quantity	Tax 5%	Total	Date	Time	Payment	cogs	gross margin percentage	gross income	Rating
0	750-67-8428	A	Yangon	Member	Female	Health and beauty	74.69	7	26.1415	548.9715	1/5/2019	13:08	Ewallet	522.83	4.761905	26.1415	9.1
1	226-31-3081	C	Naypyitaw	Normal	Female	Electronic accessories	15.28	5	3.8200	80.2200	3/8/2019	10:29	Cash	76.40	4.761905	3.8200	9.6
2	631-41-3108	A	Yangon	Normal	Male	Home and lifestyle	46.33	7	16.2155	340.5255	3/3/2019	13:23	Credit card	324.31	4.761905	16.2155	7.4
3	123-19-1176	A	Yangon	Member	Male	Health and beauty	58.22	8	23.2880	489.0480	1/27/2019	20:33	Ewallet	465.76	4.761905	23.2880	8.4
4	373-73-7910	A	Yangon	Normal	Male	Sports and travel	86.31	7	30.2085	634.3785	2/8/2019	10:37	Ewallet	604.17	4.761905	30.2085	5.3

	Unit price	Quantity	Tax 5%	Total	cogs	gross margin percentage	gross income	Rating
count	1000.000000	1000.000000	1000.000000	1000.000000	1000.00000	1.000000e+03	1000.000000	1000.00000
mean	55.672130	5.510000	15.379369	322.966749	307.58738	4.761905e+00	15.379369	6.97270
std	26.494628	2.923431	11.708825	245.885335	234.17651	6.131498e-14	11.708825	1.71858
min	10.080000	1.000000	0.508500	10.678500	10.17000	4.761905e+00	0.508500	4.00000
25%	32.875000	3.000000	5.924875	124.422375	118.49750	4.761905e+00	5.924875	5.50000
50%	55.230000	5.000000	12.088000	253.848000	241.76000	4.761905e+00	12.088000	7.00000
75%	77.935000	8.000000	22.445250	471.350250	448.90500	4.761905e+00	22.445250	8.50000
max	99.960000	10.000000	49.650000	1042.650000	993.00000	4.761905e+00	49.650000	10.00000

	Unit price	Quantity	Tax 5%	Total	cogs	gross margin percentage	gross income	Rating
Unit price	1.000000	0.010778	0.633962	0.633962	0.633962	NaN	0.633962	-0.008778
Quantity	0.010778	1.000000	0.705510	0.705510	0.705510	NaN	0.705510	-0.015815
Tax 5%	0.633962	0.705510	1.000000	1.000000	1.000000	NaN	1.000000	-0.036442
Total	0.633962	0.705510	1.000000	1.000000	1.000000	NaN	1.000000	-0.036442
cogs	0.633962	0.705510	1.000000	1.000000	1.000000	NaN	1.000000	-0.036442
gross margin percentage	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
gross income	0.633962	0.705510	1.000000	1.000000	1.000000	NaN	1.000000	-0.036442
Rating	-0.008778	-0.015815	-0.036442	-0.036442	-0.036442	NaN	-0.036442	1.000000

	Invoice ID	Branch	City	Customer type	Gender	Product line	Unit price	Quantity	Tax 5%	Total	Date	Time	Payment	cogs	gross margin percentage	gross income	Rating
166	234-65-2137	C	Naypyitaw	Normal	Male	Home and lifestyle	95.58	10	47.790	1003.590	1/16/2019	13:32	Cash	955.8	4.761905	47.790	4.8
167	687-47-8271	A	Yangon	Normal	Male	Fashion accessories	98.98	10	49.490	1039.290	2/8/2019	16:20	Credit card	989.8	4.761905	49.490	8.7
350	860-79-0874	C	Naypyitaw	Member	Female	Fashion accessories	99.30	10	49.650	1042.650	2/15/2019	14:53	Credit card	993.0	4.761905	49.650	6.6
357	554-42-2417	C	Naypyitaw	Normal	Female	Sports and travel	95.44	10	47.720	1002.120	1/9/2019	13:45	Cash	954.4	4.761905	47.720	5.2
422	271-88-8734	C	Naypyitaw	Member	Female	Fashion accessories	97.21	10	48.605	1020.705	2/8/2019	13:00	Credit card	972.1	4.761905	48.605	8.7
557	283-26-5248	C	Naypyitaw	Member	Female	Food and beverages	98.52	10	49.260	1034.460	1/30/2019	20:23	Ewallet	985.2	4.761905	49.260	4.5
699	751-41-9720	C	Naypyitaw	Normal	Male	Home and lifestyle	97.50	10	48.750	1023.750	1/12/2019	16:18	Ewallet	975.0	4.761905	48.750	8.0
792	744-16-7898	B	Mandalay	Normal	Female	Home and lifestyle	97.37	10	48.685	1022.385	1/15/2019	13:48	Credit card	973.7	4.761905	48.685	4.9
996	303-96-2227	B	Mandalay	Normal	Female	Home and lifestyle	97.38	10	48.690	1022.490	3/2/2019	17:16	Ewallet	973.8	4.761905	48.690	4.4

LOADING DATASET¶

MODELS¶

1. KNieghbors Classifier¶

2. SVM¶

3. Naive Bayes¶

4. DECISION TREE CLASSIFIER¶

EXTRATREES CLASSIFIER¶

AUC-ROC CURVE FOR TRAINING¶

AUC-ROC CURVE FOR TESTING¶

ACTUAL VS PREDICTED COMPARISON BETWEEN CLASSIFIERS¶

	Actual	Predicted
452	0	0
421	0	1
338	0	1
640	0	0
592	0	1
...	...	...
37	0	0
659	1	1
894	1	1
834	0	1
162	1	1